Gagolewski M., Scientific Impact Assessment Cannot be Fair, Journal of Informetrics 7(4), 2013, pp. 792-802.
Abstract: In this paper we deal with the problem of aggregating numeric sequences of arbitrary length that represent e.g. citation records of scientists. Impact functions are the aggregation operators that express as a single number not only the quality of individual publications, but also their author's productivity.
We examine some fundamental properties of these aggregation tools. It turns out that each impact function which always gives indisputable valuations must necessarily be trivial. Moreover, it is shown that for any set of citation records in which none is dominated by the other, we may construct an impact function that gives any a priori-established authors' ordering. Theoretically then, there is considerable room for manipulation in the hands of decision makers.
We also discuss the differences between the impact function-based and the multicriteria decision making-based approach to scientific quality management, and study how the introduction of new properties of impact functions affects the assessment process. We argue that simple mathematical tools like the h- or g-index (as well asother bibliometric impact indices) may not necessarily be a good choice when it comes to assess scientific achievements.
** FuzzyNumbers Package CHANGELOG ** *************************************************************************** 0.3-1 /2013-06-23/ * piecewiseLinearApproximation() - general case (any knot.n) for method="NearestEuclidean" now available. Thus, method="ApproximateNearestEuclidean" is now deprecated. * New binary arithmetic operators, especially for PiecewiseLinearFuzzyNumbers: +, -, *, / * New method: fapply() - applies a function on a PLFN using the extension principle * New methods: as.character(); also used by show(). This function also allows to generate LaTeX code defining the FN (toLaTeX arg thanks to Jan Caha). * as.FuzzyNumber(), as.TriangularFuzzyNumber(), as.PowerFuzzyNumber(), and as.PiecewiseLinearFuzzyNumber() are now S4 methods, and can be called on objects of type numeric, as well as on various FNs * piecewiseLinearApproximation() and as.PiecewiseLinearFuzzyNumber() argument `knot.alpha` now defaults to equally distributed knots (via given `knot.n`). If `knot.n` is missing, then it is guessed from `knot.alpha`. * PiecewiseLinearFuzzyNumber() now accepts missing `a1`, `a2`, `a3`, `a4`, and `knot.left`, `knot.right` of length `knot.n`+2. Moreover, if `knot.n` is not given, then it is guessed from length(knot.left). If `knot.alpha` is missing, then the knots will be equally distributed on the interval [0,1]. * alphacut() now always returns a named two-column matrix. evaluate() returns a named vector. * New function: TriangularFuzzyNumber - returns a TrapezoidalFuzzyNumber. * Function renamed: convert.side to convertSide, convert.alpha to convertAlpha, approx.invert to approxInvert * Added a call to setGeneric("plot", function(x, y, ...) ... to avoid a warning on install * The FuzzyNumbers Tutorial has been properly included as the package's vignette * DiscontinuousFuzzyNumber class has been marked as **EXPERIMENTAL** in the manual * Man pages extensively updated * FuzzyNumbers devel repo moved to GitHub
Abstract: Recently, a very interesting relation between symmetric minitive, maxitive, and modular aggregation operators has been shown. It turns out that the intersection between any pair of the mentioned classes is the same. This result introduces what we here propose to call the OM3 operators. In the first part of our contribution on the analysis of the OM3 operators we study some properties that may be useful when aggregating input vectors of varying lengths. In Part II we will perform a thorough simulation study of the impact of input vectors' calibration on the aggregation results.
Abstract: This article is a second part of the contribution on the analysis of the recently-proposed class of symmetric maxitive, minitive and modular aggregation operators. Recent results (Gagolewski, Mesiar, 2012) indicated some unstable behavior of the generalized h-index, which is a particular instance of OM3, in case of input data transformation. The study was performed on a small, carefully selected real-world data set. Here we conduct some experiments to examine these phenomena more extensively.